1 BACKGROUND

The prevalence of obesity, defined in adults as a body mass index (BMI; weight in kg divided by height in meters squared) ≥ 30 kg/m2, has increased dramatically worldwide over the past decades.1 An imbalance between energy intake and expenditure is regarded as the major cause of obesity. Numerous distinct characteristics and conditions can contribute to obesity within an individual.2 One important contributing factor may be chronic exposure to the stress hormone cortisol, the major end-product of the hypothalamic–pituitary–adrenal (HPA) axis. In healthy individuals, cortisol secretion and metabolism are closely linked and tightly regulated. Cortisol is converted by 11-beta-hydroxysteroid dehydrogenase type 2 (11β-HSD-2) to the biologically inactive cortisone in end-organ tissues, but can be converted back to cortisol by 11-beta-hydroxysteroid dehydrogenase type 1 (11β-HSD-1) on tissue-level.3 Exposure to very high levels of endogenous or exogenous glucocorticoids (GC), such as in Cushing's syndrome, leads to a phenotype characterized by abdominal obesity and other features of the metabolic syndrome.4, 5 It is hypothesized that even a chronic mild increase of GC, that is, in the high-physiological range, can contribute to overweight and obesity in the general population.2 Despite many efforts over the last decades to explore this relation in different matrices such as blood, saliva and urine, conflicting results were found.6 This may be due to cortisol's circadian rhythm, its pulsatile secretion, and the daily variation following changing circumstances such as acute stress. Hence, measurements that reflect a shorter term (minutes or hours for serum and saliva, days for urine) seem less suitable to investigate this association in the general population.7

In the past decennium, a relatively novel technique has allowed researchers to study long-term levels of GC by measuring cortisol and cortisone levels in scalp hair (HairF and HairE, respectively). Every centimeter of scalp hair is believed to represent the cumulative GC exposure of one month.8 HairGC measurements are now considered an easily applicable, noninvasive and reproducible method for assessing long-term GC exposure.8 A systematic review and meta-analysis by Stalder et al. that was conducted in September 2015 (when the number of studies that used HairGC started to increase rapidly) identified several possible influencers of HairF levels. The authors concluded that variation in HairF levels on study level could be related, among other factors, to differences in mean BMI of the study populations.9 Gray et al. and Ling et al. also reported that BMI and BMI standard deviation score (SDS), that is, BMI z-scores adjusted for age and sex that are most often used in pediatric studies,10 were important determinants of HairF levels in children.11, 12 However, in the last years, many new large-scale studies in various age categories have been published that have investigated the relation between HairGC and anthropometric features. Some of these studies showed a positive relation,13, 14 while other studies showed no relation between HairGC and anthropometric measurements.15, 16 It is unclear whether these conflicting results can be explained by differing population characteristics such as mean age, sex, and prevalence of obesity, use of corticosteroids, handling of outliers, or the various laboratory methods that were used.

Moreover, other anthropometric measurements than BMI are considered equally or even more relevant to cardiometabolic health, such as waist circumference (WC) and waist-hip-ratio (WHR), which both are markers of central adiposity.17 These deserve specific attention as GC are known to particularly induce abdominal obesity.18 Likewise, there are suggestions that hair cortisone might correlate stronger to obesity than hair cortisol itself.19 However, a meta-analysis that summarizes all evidence considering different anthropometric parameters in association with both HairF and HairE as well as relevant moderators of these relationships is missing.

Therefore, the aim of the current systematic review and meta-analysis was to investigate the cross-sectional relations between HairGC levels (HairF and HairE) and anthropometric measurements (BMI, BMI SDS, WC, and WHR) and to explore the possible influence of relevant characteristics of the population and laboratory methods.

2 METHODS

We performed this systematic review and meta-analysis in concordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement and Meta-analysis of Observational Studies in Epidemiology (MOOSE) checklist.20, 21 This systematic review was registered at the PROSPERO database (Registration number CRD42020205187, December 7, 2020).22

2.1 Search strategy and selection criteria

A university health sciences librarian designed a comprehensive search to identify studies and conference abstracts concerning hair cortisol and/or hair cortisone and measurements of obesity. To avoid missing potentially relevant papers we designed a broad search strategy combining the elements “hair,” “cortisol/cortisone,” and “BMI/WC/WHR/anthropometrics”, including their synonyms without any restrictions other than “studies in humans”. The search was conducted in the following databases from inception up to November 16, 2020: Medline (Ovid), Embase, Cochrane, Web of Science, Scopus, Cinahl, PsycInfo, and Google Scholar. The complete search strategy is provided in the supporting information Appendix S1. Search results were exported to reference management software (EndNote version X9, Clarivate Analytics), and duplicates were removed prior to screening.

All identified studies were independently screened in two stages by two physicians (EV, OA, or MM) with a background in adult (EV and MM) and pediatric (OA) endocrinology. All studies that reported original HairGC data in humans were included in the title/abstract screening stage and were subsequently assessed full text. Disagreements were solved by discussion among the first authors (EV, OA, and MM), and the senior author (EvR) until consensus was reached. Additionally, reference lists of all included studies and relevant reviews were screened systematically for potentially relevant articles.23 We included studies that reported cross-sectional associations between HairGC and measurements of obesity. We excluded case reports, animal studies, review articles, non-English or nonpeer reviewed studies, and studies in which hair sampling and weight measurements were not performed simultaneously (Figure 1). Pediatric studies that only included children younger than age 2 years were also excluded because BMI-based definitions of obesity are not available for this age group.10 We contacted all corresponding authors of articles that reported both HairGC and anthropometric data but did not report an association between these two outcomes to ask if they could provide us with an association measure. Of articles that also included patients with mental or physical diseases that are known to influence the relation between GCs and obesity, we only included the separate analyses of healthy controls if available. When data of the same participants were reported in several studies, we included the study that reported a bivariate association (correlation coefficient or unstandardized simple linear regression coefficient) between HairGC and measurements of obesity. If more than one article reported a bivariate association, we included the study with the largest sample size.

Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram. HairGC, hair glucocorticoids

2.2 Data extraction

Descriptive, methodological, and outcome data were extracted from all included studies by two researchers independently (EV, OA, or MM) using a predesigned standardized data extraction sheet. Discrepancies were resolved by discussion among the first authors (EV, OA, and MM) and the senior author (EvR). The following descriptive data were extracted: study population characteristics (sample size and cohort characteristics: age, sex, prevalence of obesity, mean levels of HairF and HairE in pg/mg) and laboratory methods: liquid chromatography-(tandem) mass spectrometry based measurements (LC–MS or LC–MS/MS, in this review further collectively abbreviated as LC–MS), enzyme-linked immunosorbent assays (ELISA), or chemiluminescent immunoassays (CLIA). The reported outcomes of interest were any cross-sectional associations between HairGC (HairF, HairE) and measurements of obesity, that is, BMI, BMI SDS, WC, and WHR. In studies presenting multiple data points of the same participants (e.g., before and after an intervention), only baseline associations were extracted. When insufficient data were reported for meta-analysis, corresponding authors were contacted twice in a 2-week time frame. In case of nonresponse, data were extracted from previous meta-analyses where possible.9, 12

2.3 Risk of bias assessment

Risk of bias was assessed by two researchers independently (EV, OA, or MM) using the Quality In Prognostic Studies (QUIPS) tool.24 In short, the QUIPS tool aids in the assessment of potential bias sources from the following study domains: study participation, study attrition, prognostic factor measurement, outcome measurement, confounding measurement, and statistical analysis. The subdomains on which risk of bias was assessed were the following: population selection criteria (QUIPS 1; study participation), the used laboratory methods (QUIPS 3; prognostic factor measurement), whether or not anthropometric measurements were objectively measured (QUIPS 4; outcome measurement), whether or not corticosteroid use was taken into account and whether any consideration was given to handling outliers in HairGC values (QUIPS 5; study confounding), and reporting of relevant statistics (QUIPS 6; statistical analysis and reporting). All subdomains were scored as ‘low’, ‘moderate’, or ‘high’ risk of bias on individual cohort level. We omitted the study attrition domain of the QUIPS tool (QUIPS 2) since it was not applicable to our cross-sectional research question. Discrepancies between the researchers were solved by discussion among the first authors (EV, OA, and MM) and the senior author (EvR).

2.4 Qualitative synthesis

For the qualitative synthesis, we summarized all authors' conclusions regarding cross-sectional associations between HairGC levels and obesity measurements, that is, correlation coefficients, regression coefficients, or comparison of HairGC levels and obesity measurements across categories.

2.5 Statistical analysis

All meta-analyses were conducted in R version 3.6.3 with an α of 0.05.25 For all descriptive data, medians and (interquartile) ranges were converted to means and standard deviations prior to analyses.26 Furthermore, subgroup means from individual studies as well as the pooled means across all studies were pooled.27 When not originally reported, standard errors were calculated based on reported confidence intervals or p-values and degrees of freedom using the T-distribution.

2.6 Meta-analysis of correlation coefficients

For all studies reporting bivariate correlations (correlation coefficients), Fisher's r-to-z transformation was applied to transform individual correlations stratified on all combinations of HairGC (HairF and HairE) and obesity measurements (BMI, BMI SDS, WC, and WHR). As several studies reported correlations within distinct subgroups, we calculated the pooled correlation coefficients, 95% confidence intervals (CIs) and prediction intervals (PIs) using multilevel random effects models.28, 29 One study was excluded for all meta-analyses, as the reported correlation coefficient for BMI versus HairF of the total cohort was 0.91. We assume this is a typographic error, as the authors state that they only found a statistically significant correlation in the highest tertile of the polygenic susceptibility score (which was reported to be 0.269, making a correlation of 0.91 for the total cohort impossible).30 These authors did not respond to our contact attempts.

The I2 statistic and Cochrane's Q test were used for the assessment of between-study heterogeneity, with I2 > 25% and p-value for Cochrane's Q test <0.05 indicating heterogeneity. For all meta-analyses with data from at least 10 cohorts, exploratory moderator analyses were performed using mixed-effect models for categorical parameters (e.g., used laboratory method) and random effects models for continuous parameters (e.g., mean age of the study participants). Publication bias was assessed using contour-enhanced funnel plots.

2.7 Meta-analysis of unstandardized simple linear regression coefficients

For all studies reporting unstandardized simple linear regression coefficients between 10-log transformed HairGC (HairF or HairE) in pg/mg as independent variable and untransformed obesity measurements (BMI, BMI SDS, WC, and WHR) as dependent variable, pooled regression coefficients and 95% CIs were calculated using the statistical approach described by Bini et al. and Becker & Wu.31, 32 In short, this approach allows pooling of linear regression coefficients using weighted least squares provided that the independent and dependent variables have been measured in the same manner across all studies. Therefore, we calculated pooled regression coefficients of 10-log transformed HairGC on untransformed obesity measurements, stratified on laboratory method. Between-study heterogeneity was assessed using the Qw-statistic described by Bini et al.31

3 RESULTS

The literature search identified 1017 unique citation titles of which a total of 120 studies5, 13, 14, 16, 19, 30, 33-146 comprising 146 separate cohorts were included (Figure 1). This corresponds to a total of 34,342 included participants of which 15,698 (46%) were sampled from general population-based studies (Table 1). The remaining 18,644 (54%) participants were sampled from studies where study inclusion was based on medical criteria (e.g., individuals with obesity), occupational characteristics (e.g., health-care workers), or socio-economic characteristics (e.g., children from low-income parents). The majority of participants (24,004; 70%) were sampled from studies in adults (mean age ≥18 years). Most studies analyzed participants living in Germany (32/146 cohorts, 22%), The Netherlands (23/146 cohorts, 16%), and Canada (18/146 cohorts, 12%). For 70/146 cohorts (48%), correlation coefficients and/or regression coefficients that were not reported in original papers were obtained by contacting authors.

TABLE 1. Overview of included cohorts Study n Age in years BMI in kg/m2 Or BMI SDS M ± SD % male % obesity HairF in pg/mg HairE in pg/mg HairGC analysis Risk of biasa Reported bivariate correlations Reported regression coefficients M ± SD M ± SD M ± SD Adult cohorts Abdulateef et al. (2019) 65 33.1 ± 10.4 26.4 ± 5.7 9.8 28.1 17.2 ELISA 23311 AC AC Abell et al. (2016) 3,634 69.8 ± 5.8 26.7 ± 4.5 68.4 19.8 12.6 ± 46.4 LC–MS 21121 ACD A Aguilo et al. (2018) 53 56.7 ± 12.5 25.1 ± 3.9 30.2 9.4 14.0 ± 9.0 ELISA 23321 A A Berger et al. (2019)—Cohort WPHC 207 40.3 ± 16.9 31.5 ± 7.2 44.4 14.2 ± 27.8 ELISA 23331 A Berger et al. (2019)—Cohort YPC 122 19.4 ± 3.1 25.2 ± 6.9 43.4 7.8 ± 9.3 ELISA 23331 A Boesch et al. (2014) 177 20.1 ± 1.1 23.6 ± 3.1 100 358.8 ± 159.1 ELISA 23131 A Bossé et al. (2018) 598 64.9 ± 6.8 29.3 ± 6.5 80.6 36.9 11.9 ± 26.7 CLIA 32121 AC AC Brianda et al. (2020) 134 24.6 ± 4.4 7.1 82.3 ± 94.3 ELISA 23311 A A Castro-Vale et al. (2020) 128 49.1 ± 15.5 27.0 ± 3.9 68 4.7 ± 3.7 LC–MS 21331 A A Cedillo et al. (2020) 62 29.2 ± 7.5b 30.0 ± 7.7b 0 27.0 130.7 ± 124.5b ELISA 23131 A Chan et al. (2014) 57 44.5 ± 12.5b,c 27.6 ± 6.8b,c 45.6 33.3 98.8 ± 74.8b,c ELISA 33121 AC Chen et al. (2013) 53 40.7 ± 6.6 22.4 ± 2.9 98.11 18.9 ± 13.6 LC–MS 22331 A Chen et al. (2015)—female adults 75 43.3 ± 8.9 30.2 ± 5.5 0 4.6 ± 3.4 LC–MS 21121 AD Chen et al. (2015)—male adults 10 41.6 ± 9.2 29.4 ± 1.9 100 3.1 ± 1.5 LC–MS 21121 AD Davison et al. (2019) 344 25.4 ± 1.5b c 23.7 ± 6.3 43 4.3 ± 4.9 6.3 ± 5.8 LC–MS 11121 AE Dettenborn et al. (2010)—employed 28 32.6 ± 9.3 22.6 ± 3.8 42.9 7.1 ± 3.0 CLIA 22321 A Dettenborn et al. (2010)—unemployed 31 36.7 ± 11.0 24.6 ± 6.3 3.2 10.2 ± 7.2 CLIA 22321 A Diebig et al. (2016) 129 32.3 ± 12.1 24.4 ± 4.3 24 11.6 ± 13.2 CLIA 22331 A A Dowlati et al. (2010)—controls 87 65.7 ± 11.1 27.5 ± 4.9 80.5 185.3 ± 131.6 ELISA 33321 A Enge et al. (2020) 470 38.6 ± 8.9 24.6 ± 4.9 34 8.3 6.1 ± 7.4 LC–MS 11311 A A Engert et al. (2018) 332 40.7 ± 9.2 23.6 ± 3.3 40.7 1.6 ± 1.0 2.5 ± 0.7 LC–MS 11131 AE Etwel et al. (2014) 39 23.8 ± 6.2 23.2 ± 4.8 0 257.2 ± 101.8 ELISA 23121 A Feeney et al. (2020) 1876 66.4 ± 8.7 25.6 31.5 18.8 ± 48.1 12.4 ± 10.3 LC–MS 11111 AE AE Feller et al. (2014) 654 65.8 ± 8.4 27.5 ± 4.4 46 35.1 ± 32.8 CLIA 12111 ACD Fischer et al. (2017) 139 50.6 ± 14.6 27.5 ± 6.0 28 28 ELISA 13311 A Gao et al. (2014)—adult control 23 41.5 ± 12.8 22.9 ± 2.7 61 0 4.3 ± 3.9 LC–MS 22321 A Gao et al. (2014)- adult earthquake survivor 20 45.5 ± 14.2 23.4 ± 2.1 60 0 46.3 ± 48.4 LC–MS 22321 A Garcia-Leon et al. (2018) 62 33.0 ± 3.7 22.8 ± 2.9 0 127.9 ± 111.5 ELISA 13313 A Gidlow et al. (2016) 132 41.4 ± 11.4 25.1 ± 4.8 28.9 10.8 ± 9.4 ELISA 13321 A A Grass et al. (2015)—study I 42 24.8 ± 5.7 21.3 ± 2.9 52.4 3.5 ± 2.3 LC–MS 11311 A Grass et al. (2015)—study II 52 25.0 ± 4.9 22.8 ± 3.2 57.7 3.2 ± 3.8 LC–MS 11311 A Henley et al. (2014) 109 29.8 16.2 592.2 ± 304.8b ELISA 13123 A